基于监督机器学习回归算法的6061 AA搅拌摩擦焊工艺参数优化
Process parameter optimization of Friction Stir Welding on 6061AA using Supervised Machine Learning Regression-based Algorithms
摘要：
最高强度重量比的标准吸引了好奇心越来越多的几乎所有领域的重量减少是必不可少的。轻质材料及其连接工艺也是制造业最近的研究需求点。搅拌摩擦焊（FSW）是近年来在不添加任何第三种材料（填充棒）的情况下连接材料并在低于母材熔点的温度下连接的最新进展之一。该工艺广泛用于连接相似和不同的金属，特别是轻质有色金属材料，如铝，铜和镁合金。本文提出了获得提高焊接接头的机械性能的最佳工艺参数的裁决。实验在5 mm的6061铝合金板上进行。工艺参数;工具材料、旋转速度、横向速度和轴向力被利用。采用拉伸试验对焊接接头的力学性能进行了测试，接头强度效率最高可达94.2%。使用基于监督机器学习的回归算法，例如决策树、随机森林和梯度提升算法。结果表明，随机森林算法产生最高的决定系数值为0.926，这意味着它提供了一个最好的拟合相比，其他算法。
英文摘要：
The highest strength-to-weight ratio criterion has fascinated curiosity increasingly in virtually all areas where heft reduction is indispensable. Lightweight materials and their joining processes are also a recent point of research demands in the manufacturing industries. Friction Stir Welding (FSW) is one of the recent advancements for joining materials without adding any third material (filler rod) and joining below the melting point of the parent material. The process is widely used for joining similar and dissimilar metals, especially lightweight non-ferrous materials like aluminum, copper, and magnesium alloys. This paper presents verdicts of optimum process parameters on attaining enhanced mechanical properties of the weld joint. The experiment was conducted on a 5 mm 6061 aluminum alloy sheet. Process parameters; tool material, rotational speed, traverse speed, and axial forces were utilized. Mechanical properties of the weld joint are examined employing a tensile test, and the maximum joint strength efficiency was reached 94.2%. Supervised Machine Learning based Regression algorithms such as Decision Trees, Random Forest, and Gradient Boosting Algorithm were used. The results showed that the Random Forest algorithm yielded highest coefficient of determination value of 0.926 which means it gives a best fit in comparison to other algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
